@InProceedings{rouvier:2017:SemEval,
  author    = {Rouvier, Mickael},
  title     = {LIA at SemEval-2017 Task 4: An Ensemble of Neural Networks for Sentiment Classification},
  booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {760--765},
  abstract  = {This paper describes the system developed at LIA for the SemEval-2017
	evaluation campaign. The goal of Task 4.A was to identify sentiment polarity in
	tweets. The system is an ensemble of Deep Neural Network (DNN) models:
	Convolutional Neural Network (CNN) and Recurrent Neural Network Long Short-Term
	Memory (RNN-LSTM). We initialize the input representation of DNN with different
	sets of embeddings trained on large datasets. The ensemble of DNNs are combined
	using a score-level fusion approach. The system ranked 2nd at SemEval-2017 and
	obtained an average recall of 67.6%.},
  url       = {http://www.aclweb.org/anthology/S17-2128}
}